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This work reports the empirical performance of an automated medical landmarkdetection method for predict clinical markers in hip radiograph images.Notably, the detection method was trained using a label-only augmentationscheme; our results indicate that this form of augmentation outperformstraditional data augmentation and produces highly sample efficient estimators.We train a generic U-Net-based architecture under a curriculum consisting oftwo phases: initially relaxing the landmarking task by enlarging the labelpoints to regions, then gradually eroding these label regions back to the basetask. We measure the benefits of this approach on six datasets of radiographswith gold-standard expert annotations.

Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images